FM / layers

/usr/local/lib64/python3.6/site-packages/caffe2/python/layers/

/usr/local/lib64/python3.6/site-packages/caffe2/python/layers

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
adaptive_weight.py	5687	0644	edit dl rm
add_bias.py	1396	0644	edit dl rm
arc_cosine_feature_map.py	7345	0644	edit dl rm
batch_huber_loss.py	3523	0644	edit dl rm
batch_lr_loss.py	11577	0644	edit dl rm
batch_mse_loss.py	2333	0644	edit dl rm
batch_normalization.py	3823	0644	edit dl rm
batch_sigmoid_cross_entropy_loss.py	1483	0644	edit dl rm
batch_softmax_loss.py	4580	0644	edit dl rm
blob_weighted_sum.py	2219	0644	edit dl rm
bpr_loss.py	1499	0644	edit dl rm
bucket_weighted.py	2355	0644	edit dl rm
build_index.py	1937	0644	edit dl rm
concat.py	4849	0644	edit dl rm
constant_weight.py	1208	0644	edit dl rm
conv.py	5050	0644	edit dl rm
dropout.py	1410	0644	edit dl rm
fc.py	9296	0644	edit dl rm
fc_without_bias.py	1954	0644	edit dl rm
fc_with_bootstrap.py	12788	0644	edit dl rm
feature_sparse_to_dense.py	14361	0644	edit dl rm
functional.py	4875	0644	edit dl rm
gather_record.py	3260	0644	edit dl rm
homotopy_weight.py	4306	0644	edit dl rm
label_smooth.py	3507	0644	edit dl rm
last_n_window_collector.py	2392	0644	edit dl rm
layers.py	17412	0644	edit dl rm
layer_normalization.py	4291	0644	edit dl rm
margin_rank_loss.py	1951	0644	edit dl rm
merge_id_lists.py	1500	0644	edit dl rm
pairwise_similarity.py	3549	0644	edit dl rm
position_weighted.py	2066	0644	edit dl rm
random_fourier_features.py	3187	0644	edit dl rm
reservoir_sampling.py	3013	0644	edit dl rm
sampling_train.py	2210	0644	edit dl rm
sampling_trainable_mixin.py	1366	0644	edit dl rm
select_record_by_context.py	2381	0644	edit dl rm
semi_random_features.py	5809	0644	edit dl rm
sparse_dropout_with_replacement.py	3943	0644	edit dl rm
sparse_feature_hash.py	4618	0644	edit dl rm
sparse_itemwise_dropout_with_replacement.py	3944	0644	edit dl rm
sparse_lookup.py	22170	0644	edit dl rm
split.py	2257	0644	edit dl rm
tags.py	4114	0644	edit dl rm
uniform_sampling.py	2779	0644	edit dl rm
__init__.py	943	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/layers/position_weighted.py (2066B)

## @package position_weighted
# Module caffe2.python.layers.position_weighted

import logging
import numpy as np

from caffe2.python import schema
from caffe2.python.layers.layers import (
    get_categorical_limit,
    ModelLayer,
)

from caffe2.python.layers.tags import Tags

logger = logging.getLogger(__name__)

class PositionWeighted(ModelLayer):
    def __init__(self, model, input_record, weight_optim=None,
                 name="position_weights"):
        super(PositionWeighted, self).__init__(model, name, input_record)

assert isinstance(input_record, schema.List), "Incorrect input type"
        length_metadata = input_record.lengths.metadata
        max_length = (length_metadata.categorical_limit if length_metadata is
                      not None else None)
        if max_length is not None:
            self.shape = max_length
        else:
            self.shape = get_categorical_limit(input_record)
            logger.warning(
                '{}: categorical_limit of lengths is not available, using '
                'categorical_limit of the keys: {}'.format(
                    str(input_record.lengths()), self.shape))

self.pos_w = self.create_param(param_name='pos_w',
                                       shape=[self.shape, ],
                                       initializer=('ConstantFill', {'value': 1.0}),
                                       optimizer=weight_optim)

self.output_schema = schema.Struct(
            ('position_weights',
                schema.Scalar((np.float32, self.shape),
                              self.get_next_blob_reference("pos_w_gather")))
        )

self.tags.update({Tags.HANDLE_AS_SPARSE_LAYER})

def get_memory_usage(self):
        return self.shape

def add_ops(self, net):
        inc_seq = net.LengthsRangeFill(
            [self.input_record.lengths()],
            self.input_record.lengths() + '_pos_w_seq'
        )

net.Gather(
            [self.pos_w, inc_seq],
            self.output_schema.position_weights.field_blobs())

## @package position_weighted # Module caffe2.python.layers.position_weighted import logging import numpy as np from caffe2.python import schema from caffe2.python.layers.layers import ( get_categorical_limit, ModelLayer, ) from caffe2.python.layers.tags import Tags logger = logging.getLogger(__name__) class PositionWeighted(ModelLayer): def __init__(self, model, input_record, weight_optim=None, name="position_weights"): super(PositionWeighted, self).__init__(model, name, input_record) assert isinstance(input_record, schema.List), "Incorrect input type" length_metadata = input_record.lengths.metadata max_length = (length_metadata.categorical_limit if length_metadata is not None else None) if max_length is not None: self.shape = max_length else: self.shape = get_categorical_limit(input_record) logger.warning( '{}: categorical_limit of lengths is not available, using ' 'categorical_limit of the keys: {}'.format( str(input_record.lengths()), self.shape)) self.pos_w = self.create_param(param_name='pos_w', shape=[self.shape, ], initializer=('ConstantFill', {'value': 1.0}), optimizer=weight_optim) self.output_schema = schema.Struct( ('position_weights', schema.Scalar((np.float32, self.shape), self.get_next_blob_reference("pos_w_gather"))) ) self.tags.update({Tags.HANDLE_AS_SPARSE_LAYER}) def get_memory_usage(self): return self.shape def add_ops(self, net): inc_seq = net.LengthsRangeFill( [self.input_record.lengths()], self.input_record.lengths() + '_pos_w_seq' ) net.Gather( [self.pos_w, inc_seq], self.output_schema.position_weights.field_blobs())